SIFS
Sample Importance Feature Selector
- SIFS is an automated comprehensive data quality reporting tool
- Identify important samples and features to reduce data processing load
- Unsupervised: Track changes in information content with data gathering
- Supervised: Highlight evolution in dominant data samples and features
- Data quality metrics to indicate shortcomings in data samples and features
Practical Uses
Intelligent Sampling
Extract information preserving samples for ML load reduction
Feature Selection
Identify non-redundant dominant features
Data Quality Metrics
Can your data satisfy your predictive requirements?
Data Acquisition
Indicate data samples to enhance predictive features
Active Learning
Cost effective data gathering strategies for evolving datasets
Interpretable
Tailor made for business needs for confident decision making